An introduction to the fundamental ideas in probability important for data scientists and statisticians. The core concepts of combinatorics, expectation, variance, conditional probability/expectation/distributions, covariance, and correlation will be motivated and explored through discrete and continuous random variables. The course will touch on the law of large numbers and the central limit theorem. Selected topics may be included such as the Poisson process, Linear Models, Markov chains, Bayesian methods.
Pre-reqs: Calc two (MATH 152) or instructor consent
Frequency: Taught in Fall Term
An introduction to statistical inference based on probability and calculus. Students will learn about classical and Bayesian perspectives on estimation and hypothesis testing in many contexts including numerical data, categorical data, and linear models. Some coding in R will be done for simulations. The class will explore properties of different estimation techniques such as method of moments, maximum likelihood, and Bayesian methods.
Pre-reqs: STAT 341
Frequency: Taught in Spring Term
In this course students will use linear algebra to (1) compute least-squares estimates for regression coefficients, (2) construct analysis of variance (ANOVA) and prove partition the partitioning of variability, and (3) perform prediction and statistical inference for regression models. This class will not only prove regression properties rigorously from a theoretical standpoint, but will apply models to real world data using statistical software (R).
Pre-reqs: Calc two (MATH 152) and Linear Algebra or Instructors consent
Frequency: Fall?
Willamette University